A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution

The success of search-based optimisation algorithms depends on appropriately balancing exploration and exploitation mechanisms during the course of the search. We introduce a mechanism that can be used with Differential Evolution (DE) algorithms to adaptively manage the balance between the diversification and intensification phases, depending on current progress. The method—Similarity-based Neighbourhood Search (SNS)—uses information derived from measuring Euclidean distances among solutions in the decision space to adaptively influence the choice of neighbours to be used in creating a new solution. SNS is integrated into explorative and exploitative variants of JADE, one of the most frequently used adaptive DE approaches. Furthermore, SHADE, which is another state-of-the-art adaptive DE variant, is also considered to assess the performance of the novel SNS. A thorough experimental evaluation is conducted using a well-known set of large-scale continuous problems, revealing that incorporating SNS allows the performance of both explorative and exploitative variants of DE to be significantly improved for a wide range of the test-cases considered. The method is also shown to outperform variants of DE that are hybridised with a recently proposed global search procedure, designed to speed up the convergence of that algorithm.</p

Application of Multi-Objective Evolutionary Algorithms for Planning Healthy and Balanced School Lunches

A multi-objective formulation of the Menu Planning Problem, which is termed the Multi-objective Menu Planning Problem, is presented herein. Menu planning is of great interest in the health field due to the importance of proper nutrition in today&rsquo;s society, and particularly, in school canteens. In addition to considering the cost of the meal plan as the classic objective to be minimized, we also introduce a second objective aimed at minimizing the degree of repetition of courses and food groups that a particular meal plan consists of. The motivation behind this particular multi-objective formulation is to offer a meal plan that is not only affordable but also varied and balanced from a nutritional standpoint. The plan is designed for a given number of days and ensures that the specific nutritional requirements of school-age children are satisfied. The main goal of the current work is to demonstrate the multi-objective nature of the said formulation, through a comprehensive experimental assessment carried out over a set of multi-objective evolutionary algorithms applied to different instances. At the same time, we are also interested in validating the multi-objective formulation by performing quantitative and qualitative analyses of the solutions attained when solving it. Computational results show the multi-objective nature of the said formulation, as well as that it allows suitable meal plans to be obtained

Computational Thinking and User Interfaces: A Systematic Review

Contribution: This document presents a systematic bibliographic review that demonstrates the need to conduct research on how the user experience impacts the development of computational thinking. Background: In the field of computer science, computational thinking is defined as a method that enhances problem-solving skills, system design, and human behavior understanding. Over the last few decades, several tools have been proposed for the development of computational thinking skills; however, there is no area of study that evaluates the implications or the impact that these types of platforms have on users belonging to any knowledge area. Research Question: Do user interfaces influence the development of computational thinking skills? Methodology: To address this issue, a systematic review of the literature was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology for analyzing and evaluating scientific publications. Findings: The results show that despite the dearth of literature on the subject, the specific design of a user interface has a significant impact on the development of computational thinking. Bearing the above in mind, it is necessary to conduct research that delves more deeply into the effects caused by the technologies that are used to develop computational thinking, this being a line of research that is worthy of consideration

El proyecto Piens@ Computacion@LLmente

Este trabajo describe el proyecto ‘Piens@ Computacion@LLmente’, con el que se introduce en el Pensamiento Computacional a niños de 4º de primaria y de 2º de la ESO. Para ello, se plantean diferentes ejercicios en los que hay que desarrollar una solución diseñando un algoritmo y codificándolo mediante un lenguaje de programación visual. Se proponen tanto desafíos robóticos, cómo actividades en las que no se requiere una computadora. Además, se ejecutan las intervenciones en dos modalidades, una guiada (enseñanza tradicional) y otra por descubrimiento. Finalmente, se analiza el interés que estas actividades han despertado en los alumnos, diferenciando edades, género y modalidades

A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation

In recent years, Multi-Objective Evolutionary Algorithms (MOEAS) that consider diversity as an objective have been used to tackle single-objective optimisation prob- lems. The ability to deal with premature convergence has been greatly improved with these schemes. However, they usually increase the number of free parameters that need to be tuned. To improve results and avoid the tedious hand- tuning of algorithms, the use of automated parameter con- trol approaches that are able to adapt parameter values dur- ing the course of an evolutionary run are becoming more common in the field of Evolutionary Computation (EC). This research focuses on the application of parameter control approaches to diversity-based moeas. Two external parame- ter control methods are investigated; a novel method based on Fuzzy Logic and a recently proposed Hyper-heuristic. These are compared to an internal control method that uses self- adaptation. An extensive comparison of the three methods is carried out using a set of single-objective benchmark prob- lems of diverse complexity. Analyses include comparisons to a wide range of schemes with fixed parameters and to a single-objective approach. The results show that the fuzzy logic and hyper-heuristic methods are able to find similar or better solutions than the fixed parameter methods for a sig- nificant number of problems, with considerable savings in computational resources and time, whereas the self-adaptive strategy provides little benefit. Finally, we also demonstrate that the controlled diversity-based moea outperforms the single-objective scheme in most cases, thus showing the ben- efits of solving single-objective problems through diversity-based multi-objective schemes

Engaging Primary and Secondary School Students in Computer Science Through Computational Thinking Training

Although Computer Science has grown to become one of the most highly demanded professional careers, every year, only a small percentage of students choose a degree directly related to Computer Science. Perhaps the problem lies in the lack of information that society has about Computer Science itself, and particularly about the work computer scientists do. No one doubts the role of Mathematics or Languages as core subjects in every primary and secondary education syllabus; however, Computer Science plays a negligible role in most current syllabuses. Only in a few countries have governments paid special attention to content related to Computer Science and to learning to analyze and solve problems the way computer scientists do (Computational Thinking). In this article, we present Piens@ Computacion@ULLmente , a project that provides a methodology to promote Computer Science through Computational Thinking activities among primary and secondary education students. The results obtained from an exhaustive statistical analysis of the data we collected demonstrate that the perception of Computer Science that pre-university students have can be improved through specific training. Moreover, we can also confirm that the performance of pre-university students involving Computational Thinking skills is independent of gender, particularly at the primary education level

Optimising Real-World Traffic Cycle Programs by Using Evolutionary Computation

Traffic congestion, and the consequent loss of time, money, quality of life, and higher pollution, is currently one of the most important problems in cities, and several approaches have been proposed to reduce it. In this paper, we propose a novel formulation of the traffic light scheduling problem in order to alleviate it. This novel formulation of the problem allows more realistic scenarios to be modeled, and as a result, it becomes much harder to solve in comparison to previous formulations. The proposal of more advanced and efficient techniques than those applied in past research is thus required. We propose the application of diversity-based multi-objective optimizers, which have shown to provide promising results when addressing single-objective problems. The wide experimental evaluation performed over a set of real-world instances demonstrates the good performance of our proposed diversity-based multi-objective method to tackle traffic at a large scale, especially in comparison to the best-performing single-objective optimizer previously proposed in the literature. Consequently, in this paper, we provide new state-of-the-art algorithmic schemes to address the traffic light scheduling problem that can deal with a whole city, instead of just a few streets and junctions, with a higher level of detail than the one found in present studies due to our micro-analysis of streets

DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains

To advance research in the development of optimisation algorithms, it is crucial to have access to large test-beds of diverse and discriminatory instances from a domain that can highlight strengths and weaknesses of different algorithms. The DIGNEA tool enables diverse instance suites to be generated for any domain, that are also discriminatory with respect to a set of solvers of the user choice. Written in C++, and delivered as a repository and as a Docker image, its modular and template-based design enables it to be easily adapted to multiple domains and types of solvers with minimal effort. This paper exemplifies how to generate instances for the Knapsack Problem domain

Selection methods and diversity preservation in many-objective evolutionary algorithms

Purpose – One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. Design/methodology/approach – Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group (WFG) test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings – The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value – The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of our knowledge, it has not been investigated yet

Training future engineers: Integrating Computational Thinking and effective learning methodologies into education

This article examines the effectiveness and interest generated among primary and secondary education students through activities aimed at developing Computational Thinking skills, in the context of the coronavirus disease 2019 pandemic. The shift to online or hybrid learning models posed a significant challenge for educators, particularly those lacking digital skills. The study sought to answer several research questions, including the impact of online versus in-person teaching on preuniversity students and gender differences in Computer Science perception, and Computational Thinking skills performance. The study employed a four-phase methodology, consisting of pre- and posttraining measurements of Computer Science perception and Computational Thinking skills development through specific activities delivered in-person or online. The results indicate that in-person training is more effective for developing Computational Thinking skills, particularly at the secondary education level. Furthermore, there is a need to focus on maintaining girls' interest in Computer Science during primary school, as interest levels tend to decline significantly in secondary school. These findings have significant implications for Engineering Education in the context of digital transformation and the increasing importance of Computational Thinking skills in various fields of engineering. This study highlights the importance of developing Computational Thinking skills among preuniversity students and the need for effective training methods to achieve this goal and underscore the significance of investing in Engineering Education to prepare the next generation of engineers for the rapidly changing digital landscape